splitting-datasets
Process split datasets into training, validation, and testing sets for ML model development. Use when requesting "split dataset", "train-test split", or "data partitioning". Trigger with relevant phrases based on skill purpose.
npx skills add majiayu000/claude-skill-registry --skill splitting-datasets --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Dataset Splitter This skill provides automated assistance for dataset splitter tasks. ## Overview This skill automates the process of dividing a dataset into subsets for training, validating, and testing machine learning models. It ensures proper data preparation and facilitates robust model evaluation. ## How It Works 1. **Analyze Request**: The skill analyzes the user's request to determine the dataset to be split and the desired proportions for each subset. 2. **Generate Code**: Based on the request, the skill generates Python code utilizing standard ML libraries to perform the data splitting. 3. **Execute Splitting**: The code is executed to split the dataset into training, validation, and testing sets according to the specified ratios. ## When to Use This Skill This skill activates when you need to: - Prepare a dataset for machine learning model training. - Create training, validation, and testing sets. - Partition data to evaluate model performance. ## Examples ### Example 1: Splitting a CSV file User request: "Split the data in 'my_data.csv' into 70% training, 15% validation, and 15% testing sets." The skill will: 1. Generate Python code to read the 'my_data.csv' file. 2.
- Overview
- How It Works
- When to Use This Skill
- Examples
- Example 1: Splitting a CSV file
- Example 2: Creating a Train-Test Split
- Best Practices
- Integration
- Prerequisites
- Instructions
- Output
- Error Handling
- Resources
What does the splitting-datasets skill do?
Process split datasets into training, validation, and testing sets for ML model development. Use when requesting "split dataset", "train-test split", or "data partitioning". Trigger with relevant phrases based on skill purpose.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill splitting-datasets --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.